Modulated image segmentation

ABSTRACT

A modulated segmentation system can use a modulator network to emphasize spatial prior data of an object to track the object across multiple images. The modulated segmentation system can use a segmentation network that receives spatial prior data as intermediate data that improves segmentation accuracy. The segmentation network can further receive visual guide information from a visual guide network to increase tracking accuracy via segmentation.

PRIORITY APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/192,457, filed Nov. 15, 2018, which application claims the benefit ofpriority of U.S. Provisional Application Ser. No. 62/586,637, filed onNov. 15, 2017, both of which are hereby incorporated by reference hereinin their entireties.

TECHNICAL FIELD

The present disclosure generally relates to machines special-purposemachines that manage machine learning and improvements to such variants,and to the technologies by which such special-purpose machines becomeimproved compared to other special-purpose machines for imagesegmentation using a neural networks.

BACKGROUND

Image segmentation is a computational task in which pixels correspondingto different areas of an image are labeled or assigned to categories.For example, pixels of an image of a girl holding a beverage may belabeled into different segments including: a “girl” area for pixels thatdepict the girl, and “beverage” area for pixels that depict thebeverage. A neural network (e.g., a convolutional neural network) canperform image segmentation on some computer systems. However, neuralnetwork based image segmentation requires large models that often exceedthe memory of smaller computer systems such as mobile phones. Further,implementing the models can exceed the available computational resourcesof the mobile phones (e.g., processor power or available memory).

BRIEF DESCRIPTION OF THE DRAWINGS

To easily identify the discussion of any particular element or act, themost significant digit or digits in a reference number refer to thefigure (“FIG.”) number in which that element or act is first introduced.

FIG. 1 is a block diagram showing an example messaging system forexchanging data (e.g., messages and associated content) over a network.

FIG. 2 is a block diagram illustrating further details regarding themessaging system of FIG. 1 , according to example embodiments.

FIG. 3 is a schematic diagram illustrating data which may be stored in adatabase of a messaging server system, according to certain exampleembodiments.

FIG. 4 is a schematic diagram illustrating a structure of a message,according to some embodiments, generated by a messaging clientapplication for communication.

FIG. 5 is a schematic diagram illustrating an example access-limitingprocess, in terms of which access to content (e.g., an ephemeralmessage, and associated multimedia payload of data) or a contentcollection (e.g., an ephemeral message story) may be time-limited (e.g.,made ephemeral), according to some example embodiments.

FIG. 6 shows example internal functional engines of a modulatedsegmentation system, according to some example embodiments.

FIG. 7 shows an example flow diagram of a method for implementingmodulated image segmentation, according to some example embodiments.

FIG. 8 shows an example data architecture of a modulator, according tosome example embodiments.

FIG. 9 shows an example architecture of a modulation segmentationsystem, according to some example embodiments.

FIGS. 10-12 illustrate example user interfaces for implementingmultistage neural network processing, according to some exampleembodiments.

FIG. 13 is a block diagram illustrating a representative softwarearchitecture, which may be used in conjunction with various hardwarearchitectures herein described.

FIG. 14 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

Generally, image segmentation labels image pixels of an image intodefined classes, thereby translating the image into an object map.Neural network (NN) based semi-supervised image segmentation can beperformed on every frame of a video thereby allowing an object to betracked across a number of frames as a segment or image mask. Forexample, in some embodiments, an object to be tracked in a videosequence is labeled only in the first frame of the video sequence (e.g.,the object in the first frame is annotated using an object mask) and aneural network (e.g., convolutional neural network) is tasked toidentify the object in the following frames of the video sequence. Insome embodiments, a convolutional neural network is first trained tocorrectly segment an object in a first frame, then the convolutionalneural network is trained for several hundred forward iterations andbackward iterations to adapt the convolutional neural network model tocorrectly segment the object across the entire video sequence.Performing NN-based semi-supervised image segmentation approach can beslow on low-computational power devices, e.g., mobile phones. In someexample embodiments, a modulator neural network (e.g., spatial guidemodulator) is configured to generated distributions (e.g., layerparameters) that adjust the intermediate feature maps (e.g.,intermediate feature data) of a segmentation network for a givenarbitrary network to be segmented across a video sequence. In someexample embodiments, the modulator neural network receives an image ofan object and the object's spatial prior, e.g., relative location withinthe image as indicated by a distribution, and generates multiple layersof parameters. Each parameter layer is input into another segmentationnetwork for layer-wise feature manipulation. In some exampleembodiments, a network of neural networks in a single feed-forward passreceive a video sequence and track an object in the video sequence. Insome example embodiments, the networks are differentiable and trainableusing end-to-end using stochastic gradient descent. After training, thenetwork can achieve a speed up of at least 70% faster over conventionalapproaches.

In some example embodiments, a visual guide modulator and a spatialguide modulator learn (e.g., via gradient descent training) to adjustthe intermediate feature maps in a segmentation neural network based onan annotated first frame (e.g., image mask of the object to be tracked)and the spatial location of the object (e.g., a distribution depictingthe likely location of the object). The visual guide modulator neuralnetwork takes an image mask as input, and produces channel-wiseparameters for the convolution layers of a convolutional neural networkconfigured to perform image segmentation. The spatial guide modulatorneural network takes a location prior image as input, and produceslocation-wise parameters for each convolution layer in the convolutionalneural network.

Channel-wise modulation can be implemented using conditional batchnormalization. The scale and bias parameters of each batch-normalizationlayer is produced by another modulator network, and is used to controlthe behavior of the main network for tasks such as image stylization andquestion answering. In some example embodiments, layer-wise manipulationdoes not include batch normalization of layers, and instead implementsstand-alone layers configured to perform scale-and-shift operations.Example stand-alone intermediate layers can be configured as,y _(c)=γ_(c) x _(c)+β_(c)  [1]

where x_(c) and y_(c) are the input and output feature maps in the c-thchannel, and γ_(c) and β_(c) are the modulation parameters. In this way,the design of models with modulations can be more flexible. In someexample embodiments, the scale and bias parameters are not used jointly,but rather operate independently of one another. The scale and biasparameters are controlled by different up-stream modulators and themodel can be learned jointly with both modulators contributing values,according to some example embodiments.

In some example embodiments, a conditional scale-and-shift layer (e.g.,modulated feature data) with parameters from both visual and spatialguide modulators is placed after each convolution layer in thesegmentation neural network. The visual guide modulator network produceschannel-wise parameters to adjust the weights of different channels inthe feature map, while the spatial guide modulator produces element-wisebias parameters to inject location sensitive information to themodulated features. The conditional scale-and-shift layer can beformulated as,y _(c)=γ_(c) x _(c)+β_(c)  [2]

where x_(c) and y_(c) are the input and output feature map in the c—thechannel, respectively; and where γ_(c) and β_(c) are modulationparameters from the visual and spatial guide modulators, respectively.γ_(c) is a scaler, while β_(c) is a two-dimensional vector to applypoint-wise bias values. Further details are shown in FIG. 9 , discussedbelow.

In some example embodiments, the visual guide modulator is used to adaptthe segmentation network to focus on a specific object instance, whichis the annotated object of the first frame in the scenario of one-shotvideo segmentation. The visual guide modulator extracts semanticinformation such as category, color, shape, texture from the annotatedobject and generates corresponding channel-wise weights so as tore-target the segmentation network to segment the object, potentially byfocusing on the same category, color, shape, etc. In some exampleembodiments, a Visual Geometry Group 16 (VGG16) neural network isimplemented as the model for visual guide modulator network. In someexample embodiments, all the modulation parameters are produced in thelast layer of the VGG16 model with fully-connected connections.

The visual guide modulator implicitly learns a multi-levelrepresentation of the various kinds of objects in the training data. Insome example embodiments, the training data includes Microsoft Cocodataset, a large dataset having over 80 categories of object images.Since the visual guide modulator learns the mapping from an image to avector that re-weights the features of different convolution layers inanother network, and different convolution layers learn different levelsof visual abstraction, the weights therefore embed the semanticinformation of the object on different abstraction levels. In this way,the visual guide modulator can “describe” in effect an arbitrary shapeto be tracked through an image sequence, even if the arbitrary shape isunlike anything in the training image dataset. That is, by training thevisual guide modulator to describe a large dataset, the trained visualguide modulator can describe characteristics (vector data) of thearbitrary shape to the segmentation network, such that the segmentationnetwork can effectively track the arbitrary shape through an imagesequence. Further, according to some example embodiments, the specificarea of the arbitrary shape can effectively be “focused on” usingattention data from the spatial guide modulator.

The spatial guide modulator gives a prior location of the object on theimage, e.g., the location of the object in a previous frame on the basisthat the object in the current frame is proximate to the prior location.In some example embodiments, the prior location is implemented as atwo-dimensional Gaussian distribution on the image plane (e.g., a pointspread function, PSF). The center and standard deviation of the Gaussiandistribution is computed from the predicted mask of the previous frame.The Gaussian distribution is transformed into an image and is fed intothe spatial guide modulator. The spatial guide modulator down-samplesthe image into different scales, in correspondence with the scales ofdifferent feature maps of the segmentation model, then it applies ascale-and-shift operation on each down-sampled Gaussian image togenerate the bias parameters of the modulation layers. Mathematicallythe spatial guide modulator is configured as,β_(c) =

m+

  [2]

where m is a down-sampled Gaussian image, and

and

are the scale-and-shift parameters. The bias parameters focus theattention of a given layer of the segmentation network at the scale ofthe given layer of segmentation network.

FIG. 1 shows a block diagram of an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network106. The messaging system 100 includes multiple client devices 102, eachof which hosts a number of applications including a messaging clientapplication 104. Each messaging client application 104 iscommunicatively coupled to other instances of the messaging clientapplication 104 and a messaging server system 108 via the network 106(e.g., the Internet).

Accordingly, each messaging client application 104 is able tocommunicate and exchange data with another messaging client application104 and with the messaging server system 108 via the network 106. Thedata exchanged between messaging client applications 104, and between amessaging client application 104 and the messaging server system 108,includes functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video, or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 106 to a particular messaging client application 104. Whilecertain functions of the messaging system 100 are described herein asbeing performed by either a messaging client application 104 or by themessaging server system 108, it will be appreciated that the location ofcertain functionality within either the messaging client application 104or the messaging server system 108 is a design choice. For example, itmay be technically preferable to initially deploy certain technology andfunctionality within the messaging server system 108, and to latermigrate this technology and functionality to the messaging clientapplication 104 where a client device 102 has a sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client application 104. Suchoperations include transmitting data to, receiving data from, andprocessing data generated by the messaging client application 104. Thisdata may include message content, client device information, geolocationinformation, media annotation and overlays, message content persistenceconditions, social network information, and live event information, asexamples. Data exchanges within the messaging system 100 are invoked andcontrolled through functions available via user interfaces (UIs) of themessaging client application 104.

Turning now specifically to the messaging server system 108, anapplication programming interface (API) server 110 is coupled to, andprovides a programmatic interface to, an application server 112. Theapplication server 112 is communicatively coupled to a database server118, which facilitates access to a database 120 in which is stored dataassociated with messages processed by the application server 112.

The API server 110 receives and transmits message data (e.g., commandsand message payloads) between the client devices 102 and the applicationserver 112. Specifically, the API server 110 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client application 104 in order to invoke functionalityof the application server 112. The API server 110 exposes variousfunctions supported by the application server 112, including accountregistration; login functionality; the sending of messages, via theapplication server 112, from a particular messaging client application104 to another messaging client application 104; the sending of mediafiles (e.g., images or video) from a messaging client application 104 toa messaging server application 114 for possible access by anothermessaging client application 104; the setting of a collection of mediadata (e.g., a story); the retrieval of such collections; the retrievalof a list of friends of a user of a client device 102; the retrieval ofmessages and content; the adding and deletion of friends to and from asocial graph; the location of friends within the social graph; andopening application events (e.g., relating to the messaging clientapplication 104).

The application server 112 hosts a number of applications andsubsystems, including the messaging server application 114, an imageprocessing system 116, and a social network system 122, in some exampleembodiments. The messaging server application 114 implements a number ofmessage-processing technologies and functions, particularly related tothe aggregation and other processing of content (e.g., textual andmultimedia content) included in messages received from multipleinstances of the messaging client application 104. As will be describedin further detail, the text and media content from multiple sources maybe aggregated into collections of content (e.g., called stories orgalleries). These collections are then made available, by the messagingserver application 114, to the messaging client application 104. Otherprocessor- and memory-intensive processing of data may also be performedserver-side by the messaging server application 114, in view of thehardware requirements for such processing.

The application server 112 also includes the image processing system116, which is dedicated to performing various image processingoperations, typically with respect to images or video received withinthe payload of a message at the messaging server application 114.

The social network system 122 supports various social networkingfunctions and services, and makes these functions and services availableto the messaging server application 114. To this end, the social networksystem 122 maintains and accesses an entity graph (e.g., entity graph304 in FIG. 3 ) within the database 120. Examples of functions andservices supported by the social network system 122 include theidentification of other users of the messaging system 100 with whom aparticular user has relationships or whom the particular user is“following,” and also the identification of other entities and interestsof a particular user.

The application server 112 is communicatively coupled to a databaseserver 118, which facilitates access to a database 120 in which isstored data associated with messages processed by the messaging serverapplication 114.

FIG. 2 is a block diagram illustrating further details regarding themessaging system 100, according to example embodiments. Specifically,the messaging system 100 is shown to comprise the messaging clientapplication 104 and the application server 112, which in turn embody anumber of subsystems, namely an ephemeral timer system 202, a collectionmanagement system 204, an annotation system 206, and modulatedsegmentation system 210.

The ephemeral timer system 202 is responsible for enforcing thetemporary access to content permitted by the messaging clientapplication 104 and the messaging server application 114. To this end,the ephemeral timer system 202 incorporates a number of timers that,based on duration and display parameters associated with a message orcollection of messages (e.g., a sequence of messages displayed as a liverealtime slideshow or story), selectively display and enable access tomessages and associated content via the messaging client application104. Further details regarding the operation of the ephemeral timersystem 202 are provided below.

The collection management system 204 is responsible for managingcollections of media (e.g., collections of text, image, video, and audiodata). In some examples, a collection of content (e.g., messages,including images, video, text, and audio) may be organized into an“event gallery” or an “event story.” Such a collection may be madeavailable for a specified time period, such as the duration of an eventto which the content relates. For example, content relating to a musicconcert may be made available as a “story” for the duration of thatmusic concert. The collection management system 204 may also beresponsible for publishing an icon that provides notification of theexistence of a particular collection to the user interface of themessaging client application 104.

The collection management system 204 furthermore includes a curationinterface 208 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface208 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certainembodiments, compensation may be paid to a user for inclusion ofuser-generated content into a collection. In such cases, the curationinterface 208 operates to automatically make payments to such users forthe use of their content.

The annotation system 206 provides various functions that enable a userto annotate or otherwise modify or edit media content associated with amessage. For example, the annotation system 206 provides functionsrelated to the generation and publishing of media overlays for messagesprocessed by the messaging system 100. The annotation system 206operatively supplies a media overlay (e.g., a filter) to the messagingclient application 104 based on a geolocation of the client device 102.In another example, the annotation system 206 operatively supplies amedia overlay to the messaging client application 104 based on otherinformation, such as social network information of the user of theclient device 102. A media overlay may include audio and visual contentand visual effects. Examples of audio and visual content includepictures, text, logos, animations, and sound effects. An example of avisual effect includes color overlaying. The audio and visual content orthe visual effects can be applied to a media content item (e.g., aphoto) at the client device 102. For example, the media overlay includestext that can be overlaid on top of a photograph generated by the clientdevice 102. In another example, the media overlay includes anidentification of a location (e.g., Venice Beach), a name of a liveevent, or a name of a merchant (e.g., Beach Coffee House). In anotherexample, the annotation system 206 uses the geolocation of the clientdevice 102 to identify a media overlay that includes the name of amerchant at the geolocation of the client device 102. The media overlaymay include other indicia associated with the merchant. The mediaoverlays may be stored in the database 120 and accessed through thedatabase server 118.

In one example embodiment, the annotation system 206 provides auser-based publication platform that enables users to select ageolocation on a map and upload content associated with the selectedgeolocation. The user may also specify circumstances under whichparticular content should be offered to other users. The annotationsystem 206 generates a media overlay that includes the uploaded contentand associates the uploaded content with the selected geolocation.

In another example embodiment, the annotation system 206 provides amerchant-based publication platform that enables merchants to select aparticular media overlay associated with a geolocation via a biddingprocess. For example, the annotation system 206 associates the mediaoverlay of a highest-bidding merchant with a corresponding geolocationfor a predefined amount of time.

FIG. 3 is a schematic diagram illustrating data 300 which may be storedin the database 120 of the messaging server system 108, according tocertain example embodiments. While the content of the database 120 isshown to comprise a number of tables, it will be appreciated that thedata 300 could be stored in other types of data structures (e.g., as anobject-oriented database).

The database 120 includes message data stored within a message table314. An entity table 302 stores entity data, including an entity graph304. Entities for which records are maintained within the entity table302 may include individuals, corporate entities, organizations, objects,places, events, and so forth. Regardless of type, any entity regardingwhich the messaging server system 108 stores data may be a recognizedentity. Each entity is provided with a unique identifier, as well as anentity type identifier (not shown).

The entity graph 304 furthermore stores information regardingrelationships and associations between or among entities. Suchrelationships may be social, professional (e.g., work at a commoncorporation or organization), interest-based, or activity-based, forexample.

The database 120 also stores annotation data, in the example form offilters, in an annotation table 312. Filters for which data is storedwithin the annotation table 312 are associated with and applied tovideos (for which data is stored in a video table 310) and/or images(for which data is stored in an image table 308). Filters, in oneexample, are overlays that are displayed as overlaid on an image orvideo during presentation to a recipient user. Filters may be of varioustypes, including user-selected filters from a gallery of filterspresented to a sending user by the messaging client application 104 whenthe sending user is composing a message. Other types of filters includegeolocation filters (also known as geo-filters), which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a user interface by the messaging client application 104, basedon geolocation information determined by a Global Positioning System(GPS) unit of the client device 102. Another type of filter is a datafilter, which may be selectively presented to a sending user by themessaging client application 104, based on other inputs or informationgathered by the client device 102 during the message creation process.Examples of data filters include a current temperature at a specificlocation, a current speed at which a sending user is traveling, abattery life for a client device 102, or the current time.

Other annotation data that may be stored within the image table 308 isso-called “lens” data. A “lens” may be a real-time special effect andsound that may be added to an image or a video.

As mentioned above, the video table 310 stores video data which, in oneembodiment, is associated with messages for which records are maintainedwithin the message table 314. Similarly, the image table 308 storesimage data associated with messages for which message data is stored inthe message table 314. The entity table 302 may associate variousannotations from the annotation table 312 with various images and videosstored in the image table 308 and the video table 310.

A story table 306 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a story or a gallery). The creation of a particularcollection may be initiated by a particular user (e.g., each user forwhom a record is maintained in the entity table 302). A user may createa “personal story” in the form of a collection of content that has beencreated and sent/broadcast by that user. To this end, the user interfaceof the messaging client application 104 may include an icon that isuser-selectable to enable a sending user to add specific content to hisor her personal story.

A collection may also constitute a “live story,” which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a “live story” may constitute a curated stream of user-submitted contentfrom various locations and events. Users whose client devices 102 havelocation services enabled and are at a common location or event at aparticular time may, for example, be presented with an option, via auser interface of the messaging client application 104, to contributecontent to a particular live story. The live story may be identified tothe user by the messaging client application 104 based on his or herlocation. The end result is a “live story” told from a communityperspective.

A further type of content collection is known as a “location story,”which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some embodiments, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to some embodiments, generated by a messaging clientapplication 104 for communication to a further messaging clientapplication 104 or the messaging server application 114. The content ofa particular message 400 is used to populate the message table 314stored within the database 120, accessible by the messaging serverapplication 114. Similarly, the content of a message 400 is stored inmemory as “in-transit” or “in-flight” data of the client device 102 orthe application server 112. The message 400 is shown to include thefollowing components:

-   -   A message identifier 402: a unique identifier that identifies        the message 400.    -   A message text payload 404: text, to be generated by a user via        a user interface of the client device 102 and that is included        in the message 400.    -   A message image payload 406: image data captured by a camera        component of a client device 102 or retrieved from memory of a        client device 102, and that is included in the message 400.    -   A message video payload 408: video data captured by a camera        component or retrieved from a memory component of the client        device 102, and that is included in the message 400.    -   A message audio payload 410: audio data captured by a microphone        or retrieved from the memory component of the client device 102,        and that is included in the message 400.    -   Message annotations 412: annotation data (e.g., filters,        stickers, or other enhancements) that represents annotations to        be applied to the message image payload 406, message video        payload 408, or message audio payload 410 of the message 400.    -   A message duration parameter 414: a parameter value indicating,        in seconds, the amount of time for which content of the message        400 (e.g., the message image payload 406, message video payload        408, and message audio payload 410) is to be presented or made        accessible to a user via the messaging client application 104.    -   A message geolocation parameter 416: geolocation data (e.g.,        latitudinal and longitudinal coordinates) associated with the        content payload of the message 400. Multiple message geolocation        parameter 416 values may be included in the payload, with each        of these parameter values being associated with respective        content items included in the content (e.g., a specific image in        the message image payload 406, or a specific video in the        message video payload 408).    -   A message story identifier 418: values identifying one or more        content collections (e.g., “stories”) with which a particular        content item in the message image payload 406 of the message 400        is associated. For example, multiple images within the message        image payload 406 may each be associated with multiple content        collections using identifier values.    -   A message tag 420: one or more tags, each of which is indicative        of the subject matter of content included in the message        payload. For example, where a particular image included in the        message image payload 406 depicts an animal (e.g., a lion), a        tag value may be included within the message tag 420 that is        indicative of the relevant animal. Tag values may be generated        manually, based on user input, or may be automatically generated        using, for example, image recognition.    -   A message sender identifier 422: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 on        which the message 400 was generated and from which the message        400 was sent.    -   A message receiver identifier 424: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 to        which the message 400 is addressed.

The contents (e.g., values) of the various components of the message 400may be pointers to locations in tables within which content data valuesare stored. For example, an image value in the message image payload 406may be a pointer to (or address of) a location within the image table308. Similarly, values within the message video payload 408 may point todata stored within the video table 310, values stored within the messageannotations 412 may point to data stored in the annotation table 312,values stored within the message story identifier 418 may point to datastored in the story table 306, and values stored within the messagesender identifier 422 and the message receiver identifier 424 may pointto user records stored within the entity table 302.

FIG. 5 is a schematic diagram illustrating an access-limiting process500, in terms of which access to content (e.g., an ephemeral message502, and associated multimedia payload of data) or a content collection(e.g., an ephemeral message story 504) may be time-limited (e.g., madeephemeral), according to some example embodiments.

An ephemeral message 502 is shown to be associated with a messageduration parameter 506, the value of which determines an amount of timethat the ephemeral message 502 will be displayed to a receiving user ofthe ephemeral message 502 by the messaging client application 104. Inone embodiment, where the messaging client application 104 is a socialnetwork site application client, an ephemeral message 502 is viewable bya receiving user for up to a maximum of 10 seconds, depending on theamount of time that the sending user specifies using the messageduration parameter 506.

The message duration parameter 506 and the message receiver identifier424 are shown to be inputs to a message timer 512, which is responsiblefor determining the amount of time that the ephemeral message 502 isshown to a particular receiving user identified by the message receiveridentifier 424. In particular, the ephemeral message 502 will only beshown to the relevant receiving user for a time period determined by thevalue of the message duration parameter 506. The message timer 512 isshown to provide output to a more generalized ephemeral timer system202, which is responsible for the overall timing of display of content(e.g., an ephemeral message 502) to a receiving user.

The ephemeral message 502 is shown in FIG. 5 to be included within anephemeral message story 504 (e.g., a personal story, or an event story).The ephemeral message story 504 has an associated story durationparameter 508, a value of which determines a time duration for which theephemeral message story 504 is presented and accessible to users of themessaging system 100. The story duration parameter 508, for example, maybe the duration of a music concert, where the ephemeral message story504 is a collection of content pertaining to that concert.Alternatively, a user (either the owning user or a curator user) mayspecify the value for the story duration parameter 508 when performingthe setup and creation of the ephemeral message story 504.

Additionally, each ephemeral message 502 within the ephemeral messagestory 504 has an associated story participation parameter 510, a valueof which determines the duration of time for which the ephemeral message502 will be accessible within the context of the ephemeral message story504. Accordingly, a particular ephemeral message 502 may “expire” andbecome inaccessible within the context of the ephemeral message story504, prior to the ephemeral message story 504 itself expiring in termsof the story duration parameter 508.

The ephemeral timer system 202 may furthermore operationally remove aparticular ephemeral message 502 from the ephemeral message story 504based on a determination that it has exceeded an associated storyparticipation parameter 510. For example, when a sending user hasestablished a story participation parameter 510 of 24 hours fromposting, the ephemeral timer system 202 will remove the relevantephemeral message 502 from the ephemeral message story 504 after thespecified 24 hours. The ephemeral timer system 202 also operates toremove an ephemeral message story 504 either when the storyparticipation parameter 510 for each and every ephemeral message 502within the ephemeral message story 504 has expired, or when theephemeral message story 504 itself has expired in terms of the storyduration parameter 508.

In response to the ephemeral timer system 202 determining that anephemeral message story 504 has expired (e.g., is no longer accessible),the ephemeral timer system 202 communicates with the messaging system100 (e.g., specifically, the messaging client application 104) to causean indicium (e.g., an icon) associated with the relevant ephemeralmessage story 504 to no longer be displayed within a user interface ofthe messaging client application 104.

FIG. 6 shows example internal functional engines of a modulatedsegmentation system, according to some example embodiments. Asillustrated, the modulated segmentation system comprises an image engine605, a training engine 610, a segmentation engine 615, a spatialmodulation engine 620, a visual modulation engine 625, and a displayengine 630. The image engine 605 is configured to generate one or moreimages (e.g., a video sequence) for processing using an image sensor ofa user device. The training engine 610 is configured to train the neuralnetworks (e.g., networks within segmentation engine 615, spatialmodulation engine 620, and visual modulation engine 625) on trainingdata in an end-to-end training process, as discussed with reference toFIG. 9 below. In some example embodiments, the modulated segmentationsystem 210 omits training engine 610. In those example embodiments, thetraining occurs off the user device (e.g., within a training engine onapplication server 112) and the trained model is distributed tomodulated segmentation systems on different client devices.

The segmentation engine 615 is configured to implement a convolutionalneural network to perform image segmentation. The spatial modulationengine 620 is configured to implement a neural network to generate layerparameter data for the layers in the segmentation engine 615. The visualmodulation engine 625 is configured to implement a neural network togenerate parameter data that describes an arbitrary shape to be trackedand segmented across images generated by the image engine 605. Thedisplay engine 630 is configured to use the image mask generated by theother engines and apply an image effect to the images to generatemodified images. For example, the display engine 630 may comprise astyle transfer neural network (e.g., CycleGAN) to transfer the styleimage from a first style to a second style. The display engine 630 canfurther be configured to publish the modified images an ephemeralmessage 502 or forward output data (e.g., mask data, segmentation, layerdata, modified image data) to other components for processing asdiscussed above, according to some example embodiments.

FIG. 7 shows an example flow diagram of a method 700 for implementingimage segmentation using a network of neural nets, according to someexample embodiments. Although the operations of method 700 are displayedas an sequence of operations, it is appreciated that multiple of theoperations may be performed in parallel (e.g., operations 710, 715, and720 may be performed in parallel and receive inputs from one another).

At operation 705, the image engine 605 generates one or more imagesusing an image sensor of the client device. At operation 710, the visualmodulation engine 625 generates shape parameters that describe a shapedepicted in the image generated at operation 705. At operation 715, thespatial modulation engine 620 generates spatial parameters thatemphasize the location of the object depicted in the image of operation705. In some example embodiments, the spatial parameters may begenerated using an image generated before the image of operation 705. Atoperation 720, the segmentation engine 615 generates an image mask forthe object depicted in the image of operation 705. At operation 725, thedisplay engine 630 modifies the image of 705 to generate a new image(e.g., a girl with a frown instead of a smile, as discussed below withreference to FIGS. 10-12 ). At operation 730, the display engine 630publishes the modified image as an ephemeral message on a network site(e.g., website).

FIG. 8 shows an example architecture 800 of a visual guide modulator,according to some example embodiments. In FIG. 8 , a modulator 805 takesthe object image 810 (e.g., a man doing a handstand while breakdancing)and object image spatial prior 815 (e.g., a Gaussian distribution, apoint spread function) as inputs, and produces a list of layer-wiseparameters. The layer wise parameters are input into a segmentationnetwork 820 for layer-wise feature manipulation. The segmentationnetwork 820 can receive an image 825 as input for segmentationprocessing. The image 825 may depict the same object or an objectsimilar to the object depicted in object image 810. For example, objectimage 810 may depict a man doing a handstand while breakdancing and theimage 825 may be a later image of the same video sequence that depictsthe same man on the his back while breakdancing. The segmentationnetwork 820 generates intermediate feature maps, which are adjustedlayer wise by the layer-wise parameter inputs generated by the modulator805. The output is an image mask 830 indicating the segment (e.g., imagemask) of an object depicted in the image 825. The image mask 830 canthen be used to track the object in the image 825 and/or apply an effectto the object even though the object in the image does not exactly matchthe object used for training (e.g., object image 810). In this way, thesegmentation network 820 can generate image masks for objects neverbefore seen by the segmentation network 820.

FIG. 9 shows an example data architecture 900 of a modulationsegmentation system 210, according to some example embodiments. In theexample of FIG. 9 , the modulation segmentation system 210 comprisesthree neural networks: a visual guide modulator 905, a segmentationnetwork 910, and a spatial guide modulator 915. A video sequence servesas three kinds of inputs. First, the video sequence is used in thevisual guide modulator 905 to generate layer parameters. The videosequence is further input into the segmentation network 910 forsegmentation or image mask generation. Further, the video sequenceprovides spatial guide data for the object being tracked. For example,the spatial guide modulator 915 tracks the person dancing in the videosequence and uses a Gaussian object (e.g., point spread function) tofocus more attention on the area of the image in which the dancer iscurrently located. As illustrated, the outputs of the visual guidemodulator 905 and the spatial guide modulator 915 modify theintermediate feature representations of hidden layers in thesegmentation network 910. The output 950 is an image mask of the object,e.g., an image mask of the breakdancer in the current frame (“T FRAME”),that can be produced even though the segmentation network 910 has neverseen the specific object depicted in the video sequence (that is, theobject in “T-FRAME” is a breakdancer in a new configuration, differentfrom the dancer's configuration in the “FIRST FRAME”).

FIGS. 10-12 illustrate example user interfaces for implementingmodulated segmentation system 210, according to some exampleembodiments. As illustrated in FIG. 10 , image 1000 is an example of animage captured at operation 705 of FIG. 7 . The image 1000 depicts asmiling girl holding her hat and a tasty beverage. The image may be acurrent frame of a video sequence, where previous frames captured beforethe current frame were previously displayed on the client device. A user(e.g., the girl or another person holding a mobile phone taking apicture of the girl) may have selected button 1005 to initiate stylingof the image 1000. An image mask may be required to perform the styling(e.g., the image mask is a mouth mask that labels pixels depicting themouth area of the girl).

Responsive to selection of the button 1005 image segmentation isperformed as discussed above (e.g., the first frame of the videosequence is used as a visual guide, the previous frame is used togenerate a spatial guide, and both inputs are used to improvesegmentation performed by a segmentation network). FIG. 11 shows asegmented image 1100, which has been derived by performing imagesegmentation on image 1000 of FIG. 10 using the modulated segmentationsystem 210. The segmented image 1100 denotes different areas of theimage, including for example a hat area 1105 (labeled “1”), skin areasthat are not part of the face 1110 (labeled “2”), a face area 1115(labeled “3”), and a clothes area 1120 (labeled “4”). The differentlabel values may be included as channel data for each pixel (e.g., aforth channel in addition to RGB (Red/Blue/Green) channels). Further,the label values may be stored as a separate image having the sameheight and width as image 1000. In some example embodiments, only asingle area of the image needs to be tracked, and the sys 210 segmentsone area. For example, if only the face area needs to be tracked toapply a face based visual effect, then only the face area 1115 may besegmented or otherwise included as an image mask.

FIG. 12 shows an example modified image 1200 which has undergone styletransfer from a smile style to a frown style using an image mask (e.g.,an eye area image mask, a mouth area image mask). In some exampleembodiments, a style transfer neural network is initiated to performstyle transfer using a set of training data of people frowning andsmiling, as is understood by one of ordinary skill in the art. After themodified image is generated, it can be published via the display engine625 as an ephemeral message 502.

FIG. 13 is a block diagram illustrating an example software architecture1306, which may be used in conjunction with various hardwarearchitectures herein described. FIG. 13 is a non-limiting example of asoftware architecture, and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 1306 may execute on hardwaresuch as a machine 1300 of FIG. 13 that includes, among other things,processors, memory, and I/O components. A representative hardware layer1352 is illustrated and can represent, for example, the machine 1300 ofFIG. 13 . The representative hardware layer 1352 includes a processingunit 1354 having associated executable instructions 1304. The executableinstructions 1304 represent the executable instructions of the softwarearchitecture 1306, including implementation of the methods, components,and so forth described herein. The hardware layer 1352 also includes amemory/storage 1356, which also has the executable instructions 1304.The hardware layer 1352 may also comprise other hardware 1358.

In the example architecture of FIG. 13 , the software architecture 1306may be conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 1306may include layers such as an operating system 1302, libraries 1320,frameworks/middleware 1318, applications 1316, and a presentation layer1314. Operationally, the applications 1316 and/or other componentswithin the layers may invoke API calls 1308 through the software stackand receive a response in the form of messages 131 bb 2. The layersillustrated are representative in nature and not all softwarearchitectures have all layers. For example, some mobile orspecial-purpose operating systems may not provide aframeworks/middleware 1318, while others may provide such a layer. Othersoftware architectures may include additional or different layers.

The operating system 1302 may manage hardware resources and providecommon services. The operating system 1302 may include, for example, akernel 1322, services 1324, and drivers 1326. The kernel 1322 may act asan abstraction layer between the hardware and the other software layers.For example, the kernel 1322 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 1324 may provideother common services for the other software layers. The drivers 1326are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1326 include display drivers, cameradrivers, Bluetooth® drivers, flash memory drivers, serial communicationdrivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers,audio drivers, power management drivers, and so forth depending on thehardware configuration.

The libraries 1320 provide a common infrastructure that is used by theapplications 1316 and/or other components and/or layers. The libraries1320 provide functionality that allows other software components toperform tasks in an easier fashion than by interfacing directly with theunderlying operating system 1302 functionality (e.g., kernel 1322,services 1324, and/or drivers 1326). The libraries 1320 may includesystem libraries 1344 (e.g., C standard library) that may providefunctions such as memory allocation functions, string manipulationfunctions, mathematical functions, and the like. In addition, thelibraries 1320 may include API libraries 1346 such as media libraries(e.g., libraries to support presentation and manipulation of variousmedia formats such as MPEG4, H.264, MP3, AAC, AMR, JPG, or PNG),graphics libraries (e.g., an OpenGL framework that may be used to render2D and 3D graphic content on a display), database libraries (e.g.,SQLite that may provide various relational database functions), weblibraries (e.g., WebKit that may provide web browsing functionality),and the like. The libraries 1320 may also include a wide variety ofother libraries 1348 to provide many other APIs to the applications 1316and other software components/modules.

The frameworks/middleware 1318 provide a higher-level commoninfrastructure that may be used by the applications 1316 and/or othersoftware components/modules. For example, the frameworks/middleware 1318may provide various graphic user interface (GUI) functions, high-levelresource management, high-level location services, and so forth. Theframeworks/middleware 1318 may provide a broad spectrum of other APIsthat may be utilized by the applications 1316 and/or other softwarecomponents/modules, some of which may be specific to a particularoperating system 1302 or platform.

The applications 1316 include built-in applications 1338 and/orthird-party applications 1340. Examples of representative built-inapplications 1338 may include, but are not limited to, a contactsapplication, a browser application, a book reader application, alocation application, a media application, a messaging application,and/or a game application. The third-party applications 1340 may includean application developed using the ANDROID™ or IOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platform,and may be mobile software running on a mobile operating system such asIOS™, ANDROID™, WINDOWS® Phone, or other mobile operating systems. Thethird-party applications 1340 may invoke the API calls 1308 provided bythe mobile operating system (such as the operating system 1302) tofacilitate functionality described herein.

The applications 1316 may use built-in operating system functions (e.g.,kernel 1322, services 1324, and/or drivers 1326), libraries 1320, andframeworks/middleware 1318 to create user interfaces to interact withusers of the system. Alternatively, or additionally, in some systems,interactions with a user may occur through a presentation layer, such asthe presentation layer 1314. In these systems, the application/component“logic” can be separated from the aspects of the application/componentthat interact with a user.

FIG. 14 is a block diagram illustrating components of a machine 1400,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 14 shows a diagrammatic representation of the machine1400 in the example form of a computer system, within which instructions1416 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1400 to perform any oneor more of the methodologies discussed herein may be executed. As such,the instructions 1416 may be used to implement modules or componentsdescribed herein. The instructions 1416 transform the general,non-programmed machine 1400 into a particular machine 1400 programmed tocarry out the described and illustrated functions in the mannerdescribed. In alternative embodiments, the machine 1400 operates as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 1400 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1400 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), apersonal digital assistant (PDA), an entertainment media system, acellular telephone, a smartphone, a mobile device, a wearable device(e.g., a smart watch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 1416, sequentially or otherwise, that specify actions to betaken by the machine 1400. Further, while only a single machine 1400 isillustrated, the term “machine” shall also be taken to include acollection of machines that individually or jointly execute theinstructions 1416 to perform any one or more of the methodologiesdiscussed herein.

The machine 1400 may include processors 1410, memory/storage 1430, andI/O components 1450, which may be configured to communicate with eachother such as via a bus 1402. The memory/storage 1430 may include amemory 1432, such as a main memory, or other memory storage, and astorage unit 1436, both accessible to the processors 1410 such as viathe bus 1402. The storage unit 1436 and memory 1432 store theinstructions 1416 embodying any one or more of the methodologies orfunctions described herein. The instructions 1416 may also reside,completely or partially, within the memory 1432, within the storage unit1436, within at least one of the processors 1410 (e.g., within theprocessor cache memory accessible to processors 1412 or 1414), or anysuitable combination thereof, during execution thereof by the machine1400. Accordingly, the memory 1432, the storage unit 1436, and thememory of the processors 1410 are examples of machine-readable media.

The I/O components 1450 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1450 that are included in a particular machine 1400 willdepend on the type of machine. For example, portable machines such asmobile phones will likely include a touch input device or other suchinput mechanisms, while a headless server machine will likely notinclude such a touch input device. It will be appreciated that the I/Ocomponents 1450 may include many other components that are not shown inFIG. 14 . The I/O components 1450 are grouped according to functionalitymerely for simplifying the following discussion and the grouping is inno way limiting. In various example embodiments, the I/O components 1450may include output components 1452 and input components 1454. The outputcomponents 1452 may include visual components (e.g., a display such as aplasma display panel (PDP), a light-emitting diode (LED) display, aliquid-crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 1454 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstruments), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 1450 may includebiometric components 1456, motion components 1458, environmentcomponents 1460, or position components 1462 among a wide array of othercomponents. For example, the biometric components 1456 may includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram-basedidentification), and the like. The motion components 1458 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environment components 1460 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gassensors to detect concentrations of hazardous gases for safety or tomeasure pollutants in the atmosphere), or other components that mayprovide indications, measurements, or signals corresponding to asurrounding physical environment. The position components 1462 mayinclude location sensor components (e.g., a GPS receiver component),altitude sensor components (e.g., altimeters or barometers that detectair pressure from which altitude may be derived), orientation sensorcomponents (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1450 may include communication components 1464operable to couple the machine 1400 to a network 1480 or devices 1470via a coupling 1482 and a coupling 1472, respectively. For example, thecommunication components 1464 may include a network interface componentor other suitable device to interface with the network 1480. In furtherexamples, the communication components 1464 may include wiredcommunication components, wireless communication components, cellularcommunication components, near field communication (NFC) components,Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components,and other communication components to provide communication via othermodalities. The devices 1470 may be another machine or any of a widevariety of peripheral devices (e.g., a peripheral device coupled via aUSB).

Moreover, the communication components 1464 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1464 may include radio frequency identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional barcodes such as Universal Product Code (UPC) barcode,multi-dimensional barcodes such as Quick Response (QR) code, Aztec code,Data Matrix, Dataglyph, MaxiCode, PDF418, Ultra Code, UCC RSS-2Dbarcode, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1464, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

GLOSSARY

“CARRIER SIGNAL” in this context refers to any intangible medium that iscapable of storing, encoding, or carrying instructions 1416 forexecution by the machine 1400, and includes digital or analogcommunications signals or other intangible media to facilitatecommunication of such instructions 1416. Instructions 1416 may betransmitted or received over the network 1480 using a transmissionmedium via a network interface device and using any one of a number ofwell-known transfer protocols.

“CLIENT DEVICE” in this context refers to any machine 1400 thatinterfaces to a network 1480 to obtain resources from one or more serversystems or other client devices 102. A client device 102 may be, but isnot limited to, a mobile phone, desktop computer, laptop, PDA,smartphone, tablet, ultrabook, netbook, multi-processor system,microprocessor-based or programmable consumer electronics system, gameconsole, set-top box, or any other communication device that a user mayuse to access a network 1480.

“COMMUNICATIONS NETWORK” in this context refers to one or more portionsof a network 1480 that may be an ad hoc network, an intranet, anextranet, a virtual private network (VPN), a local area network (LAN), awireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), the Internet, a portion of theInternet, a portion of the Public Switched Telephone Network (PSTN), aplain old telephone service (POTS) network, a cellular telephonenetwork, a wireless network, a Wi-Fi® network, another type of network,or a combination of two or more such networks. For example, a network ora portion of a network 1480 may include a wireless or cellular networkand the coupling 1482 may be a Code Division Multiple Access (CDMA)connection, a Global System for Mobile communications (GSM) connection,or another type of cellular or wireless coupling. In this example, thecoupling may implement any of a variety of types of data transfertechnology, such as Single Carrier Radio Transmission Technology(1×RTT), Evolution-Data Optimized (EVDO) technology, General PacketRadio Service (GPRS) technology, Enhanced Data rates for GSM Evolution(EDGE) technology, third Generation Partnership Project (3GPP) including3G, fourth generation wireless (4G) networks, Universal MobileTelecommunications System (UMTS), High-Speed Packet Access (HSPA),Worldwide Interoperability for Microwave Access (WiMAX), Long-TermEvolution (LTE) standard, others defined by various standard-settingorganizations, other long-range protocols, or other data transfertechnology.

“EMPHEMERAL MESSAGE” in this context refers to a message 400 that isaccessible for a time-limited duration. An ephemeral message 502 may bea text, an image, a video, and the like. The access time for theephemeral message 502 may be set by the message sender. Alternatively,the access time may be a default setting or a setting specified by therecipient. Regardless of the setting technique, the message 400 istransitory.

“MACHINE-READABLE MEDIUM” in this context refers to a component, adevice, or other tangible media able to store instructions 1416 and datatemporarily or permanently and may include, but is not limited to,random-access memory (RAM), read-only memory (ROM), buffer memory, flashmemory, optical media, magnetic media, cache memory, other types ofstorage (e.g., erasable programmable read-only memory (EPROM)), and/orany suitable combination thereof. The term “machine-readable medium”should be taken to include a single medium or multiple media (e.g., acentralized or distributed database, or associated caches and servers)able to store instructions 1416. The term “machine-readable medium”shall also be taken to include any medium, or combination of multiplemedia, that is capable of storing instructions 1416 (e.g., code) forexecution by a machine 1400, such that the instructions 1416, whenexecuted by one or more processors 1410 of the machine 1400, cause themachine 1400 to perform any one or more of the methodologies describedherein. Accordingly, a “machine-readable medium” refers to a singlestorage apparatus or device, as well as “cloud-based” storage systems orstorage networks that include multiple storage apparatus or devices. Theterm “machine-readable medium” excludes signals per se.

“COMPONENT” in this context refers to a device, a physical entity, orlogic having boundaries defined by function or subroutine calls, branchpoints, APIs, or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions. Componentsmay constitute either software components (e.g., code embodied on amachine-readable medium) or hardware components. A “hardware component”is a tangible unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleembodiments, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware components of a computer system (e.g., a processor 1412 ora group of processors 1410) may be configured by software (e.g., anapplication or application portion) as a hardware component thatoperates to perform certain operations as described herein. A hardwarecomponent may also be implemented mechanically, electronically, or anysuitable combination thereof. For example, a hardware component mayinclude dedicated circuitry or logic that is permanently configured toperform certain operations. A hardware component may be aspecial-purpose processor, such as a field-programmable gate array(FPGA) or an application-specific integrated circuit (ASIC). A hardwarecomponent may also include programmable logic or circuitry that istemporarily configured by software to perform certain operations. Forexample, a hardware component may include software executed by ageneral-purpose processor or other programmable processor. Onceconfigured by such software, hardware components become specificmachines (or specific components of a machine 1400) uniquely tailored toperform the configured functions and are no longer general-purposeprocessors 1410. It will be appreciated that the decision to implement ahardware component mechanically, in dedicated and permanently configuredcircuitry, or in temporarily configured circuitry (e.g., configured bysoftware) may be driven by cost and time considerations. Accordingly,the phrase “hardware component” (or “hardware-implemented component”)should be understood to encompass a tangible entity, be that an entitythat is physically constructed, permanently configured (e.g.,hardwired), or temporarily configured (e.g., programmed) to operate in acertain manner or to perform certain operations described herein.

Considering embodiments in which hardware components are temporarilyconfigured (e.g., programmed), each of the hardware components need notbe configured or instantiated at any one instance in time. For example,where a hardware component comprises a general-purpose processor 1412configured by software to become a special-purpose processor, thegeneral-purpose processor 1412 may be configured as respectivelydifferent special-purpose processors (e.g., comprising differenthardware components) at different times. Software accordingly configuresa particular processor 1412 or processors 1410, for example, toconstitute a particular hardware component at one instance of time andto constitute a different hardware component at a different instance oftime.

Hardware components can provide information to, and receive informationfrom, other hardware components. Accordingly, the described hardwarecomponents may be regarded as being communicatively coupled. Wheremultiple hardware components exist contemporaneously, communications maybe achieved through signal transmission (e.g., over appropriate circuitsand buses) between or among two or more of the hardware components. Inembodiments in which multiple hardware components are configured orinstantiated at different times, communications between or among suchhardware components may be achieved, for example, through the storageand retrieval of information in memory structures to which the multiplehardware components have access. For example, one hardware component mayperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further hardwarecomponent may then, at a later time, access the memory device toretrieve and process the stored output. Hardware components may alsoinitiate communications with input or output devices, and can operate ona resource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors 1410 that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors 1410 may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors1410. Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor 1412 or processors1410 being an example of hardware. For example, at least some of theoperations of a method may be performed by one or more processors 1410or processor-implemented components. Moreover, the one or moreprocessors 1410 may also operate to support performance of the relevantoperations in a “cloud computing” environment or as a “software as aservice” (SaaS). For example, at least some of the operations may beperformed by a group of computers (as examples of machines 1400including processors 1410), with these operations being accessible via anetwork 1480 (e.g., the Internet) and via one or more appropriateinterfaces (e.g., an API). The performance of certain of the operationsmay be distributed among the processors 1410, not only residing within asingle machine 1400, but deployed across a number of machines 1400. Insome example embodiments, the processors 1410 or processor-implementedcomponents may be located in a single geographic location (e.g., withina home environment, an office environment, or a server farm). In otherexample embodiments, the processors 1410 or processor-implementedcomponents may be distributed across a number of geographic locations.

“PROCESSOR” in this context refers to any circuit or virtual circuit (aphysical circuit emulated by logic executing on an actual processor1412) that manipulates data values according to control signals (e.g.,“commands,” “op codes,” “machine code,” etc.) and which producescorresponding output signals that are applied to operate a machine 1400.A processor may, for example, be a central processing unit (CPU), areduced instruction set computing (RISC) processor, a complexinstruction set computing (CISC) processor, a graphics processing unit(GPU), a digital signal processor (DSP), an ASIC, a radio-frequencyintegrated circuit (RFIC), or any combination thereof. A processor 1410may further be a multi-core processor 1410 having two or moreindependent processors 1412, 1414 (sometimes referred to as “cores”)that may execute instructions 1416 contemporaneously.

“TIMESTAMP” in this context refers to a sequence of characters orencoded information identifying when a certain event occurred, forexample giving date and time of day, sometimes accurate to a smallfraction of a second.

The invention claimed is:
 1. A method comprising: generating, by one ormore processors of a user device, an image depicting an object;generating shape parameters that describe a shape of the object;generating spatial parameters that emphasize a location of the objectdepicted in the image; generating image mask data for the object basedon the shape parameters and the spatial parameters; and generating amodified image from the image mask data and the image.
 2. The method ofclaim 1, wherein generating spatial parameters further comprises:generating spatial parameters using a previous image that is generatedprior to the image.
 3. The method of claim 1, further comprising:generating multiple layer parameters using a first neural network, eachlayer parameter configured to modify intermediate feature data of asecond neural network by: generating the shape parameters that describethe shape of the object; and generating the spatial parameters thatemphasize the location of the object depicted in the image.
 4. Themethod of claim 3, wherein generating the image mask data furthercomprises: generating the image mask data for the object using thesecond neural network, the second neural network comprising a pluralityof intermediate layers configured to generate modulated feature datausing the multiple layer parameters generated by the first neuralnetwork.
 5. The method of claim 3, further comprising: training thefirst neural network and second neural network on training data usinggradient descent.
 6. The method of claim 3, wherein the second neuralnetwork is trained on training data that does not include the objectdepicted in the image, and wherein the first neural network and thesecond neural network are trained using end-to-end training.
 7. Themethod of claim 6, wherein the training data comprises images ofdifferent objects.
 8. The method of claim 7, wherein the image mask dataindicates pixel locations of one of the different objects.
 9. The methodof claim 3, wherein the first neural network generates the multiple setsof layer parameters using, as inputs, a shape object and a spatial priorof the shape object, wherein the shape object has a different shape thana shape used to generate the image mask data, wherein the spatial prioris a Gaussian distribution.
 10. The method of claim 1, furthercomprising: storing the image mask data on the user device; andpublishing the modified image as an ephemeral message on a network site.11. A system comprising: one or more processors of a machine; and amemory storing instructions that, when executed by the one or moreprocessors, cause the machine to perform operations comprising:generating an image depicting an object; generating shape parametersthat describe a shape of the object; generating spatial parameters thatemphasize a location of the object depicted in the image; generatingimage mask data for the object based on the shape parameters and thespatial parameters; and generating a modified image from the image maskdata and the image.
 12. The system of claim 11, wherein generatingspatial parameters further comprises: generating spatial parametersusing a previous image that is generated prior to the image.
 13. Thesystem of claim 11, wherein the operations further comprise: generatingmultiple layer parameters using a first neural network, each layerparameter configured to modify intermediate feature data of a secondneural network by: generating the shape parameters that describe theshape of the object; and generating the spatial parameters thatemphasize the location of the object depicted in the image.
 14. Thesystem of claim 13, wherein generating the image mask data furthercomprises: generating the image mask data for the object using thesecond neural network, the second neural network comprising a pluralityof intermediate layers configured to generate modulated feature datausing the multiple layer parameters generated by the first neuralnetwork.
 15. The system of claim 13, wherein the operations furthercomprise: training the first neural network and second neural network ontraining data using gradient descent.
 16. The system of claim 13,wherein the second neural network is trained on training data that doesnot include the object depicted in the image, and wherein the firstneural network and the second neural network are trained usingend-to-end training.
 17. The system of claim 16, wherein the trainingdata comprises images of different objects.
 18. The system of claim 17,wherein the image mask data indicates pixel locations of one of thedifferent objects.
 19. The system of claim 13, wherein the first neuralnetwork generates the multiple sets of layer parameters using, asinputs, a shape object and a spatial prior of the shape object, whereinthe shape object has a different shape than a shape used to generate theimage mask data, wherein the spatial prior is a Gaussian distribution.20. A non-transitory machine-readable storage device embodyinginstructions that, when executed by a machine, cause the machine toperform operations comprising: generating, by one or more processors ofthe machine, an image depicting an object; generating shape parametersthat describe a shape of the object; generating spatial parameters thatemphasize a location of the object depicted in the image; generatingimage mask data for the object based on the shape parameters and thespatial parameters; and generating a modified image from the image maskdata and the image.